Location of plant species in Norway gathered as a part of a survey vegetation mapping programme

Bryn, Anders; Kristoffersen, Hans-Petter; Angeloff, Michael; Nystuen, Ingvild; Aune-Lundberg, Linda; Endresen, Dag; Svindseth, Christian; Rekdal, Yngve

doi:10.1016/j.dib.2015.10.014

Cited by 613 publications

(8 citation statements)

References 3 publications

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“…An evaluation dataset was compiled by merging two vegetation datasets (Table ) that were collected using the same classification scheme and methodology, but independently of the training data: (a) a regional 9 km × 9 km grid (AR9X9) established as a supplement to AR18X18 to obtain more precise regional statistics (Bryn et al., ); (b) a subset of project‐based vegetation maps produced in the period 2004–2014 (NIBIO, ). The spatial coverage of the evaluation dataset is depicted in Appendix .…”

Section: Methodsmentioning

confidence: 99%

“…Despite the details in classification, traditional field-based vegetation mapping techniques are expensive in terms of time and costs (MNHN-EEA, 2014). In Norway, only approximately 10% of the land area has been mapped by such methods (Bryn et al, 2015) and after more than 50 years with vegetation mapping, no European country is covered wall to wall with high-resolution vegetation maps (MNHN-EEA, 2014). This means that many areas of high natural value remain to be detected and their vulnerability assessed (Keith, Elith, & Simpson, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Distribution modelling of vegetation types based on area frame survey data

Horvath

Halvorsen

Stordal

et al. 2019

Applied Vegetation Science

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Aim Many countries lack informative, high‐resolution, wall‐to‐wall vegetation or land cover maps. Such maps are useful for land use and nature management, and for input to regional climate and hydrological models. Land cover maps based on remote sensing data typically lack the required ecological information, whereas traditional field‐based mapping is too expensive to be carried out over large areas. In this study, we therefore explore the extent to which distribution modelling (DM) methods are useful for predicting the current distribution of vegetation types (VT) on a national scale. Location Mainland Norway, covering ca. 324,000 km2. Methods We used presence/absence data for 31 different VTs, mapped wall‐to‐wall in an area frame survey with 1081 rectangular plots of 0.9 km2. Distribution models for each VT were obtained by logistic generalised linear modelling, using stepwise forward selection with an F‐ratio test. A total of 116 explanatory variables, recorded in 100 m × 100 m grid cells, were used. The 31 models were evaluated by applying the AUC criterion to an independent evaluation dataset. Results Twenty‐one of the 31 models had AUC values higher than 0.8. The highest AUC value (0.989) was obtained for Poor/rich broadleaf deciduous forest, whereas the lowest AUC (0.671) was obtained for Lichen and heather spruce forest. Overall, we found that rare VTs are predicted better than common ones, and coastal VTs are predicted better than inland ones. Conclusions Our study establishes DM as a viable tool for spatial prediction of aggregated species‐based entities such as VTs on a regional scale and at a fine (100 m) spatial resolution, provided relevant predictor variables are available. We discuss the potential uses of distribution models in utilizing large‐scale international vegetation surveys. We also argue that predictions from such models may improve parameterisation of vegetation distribution in earth system models.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distribution modelling of vegetation types based on area frame survey data

Horvath

Halvorsen

Stordal

et al. 2019

Applied Vegetation Science

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“…drawing lines between fuzzy borders, deciding on vegetation type among closely related types, and recording the cover of specific species important for the separation between types (Bryn et al. ). Mosaic polygons were only used for cases where two vegetation types frequently occurred together in areas below the minimum size for polygons.…”

Section: Methodsmentioning

confidence: 99%

Distribution modelling of vegetation types in the boreal–alpine ecotone

Ullerud

Bryn

Klanderud

2016

Applied Vegetation Science

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Abbreviations DM = distribution modelling; AUC = area under the receiver operating characteristic curve; DEM = digital elevation model; MVEV = measure of variation in environmental variables; P-O = presence-only. AbstractQuestions: Vegetation mapping based on field surveys is time-consuming and expensive. Distribution modelling might be used to overcome these challenges. What is the performance of distribution modelling of vegetation compared to traditional vegetation mapping when projected locally? Does the modelling performance vary among ecosystems? Does vegetation type distribution and abundance influence the modelling performance?Location: Gravfjellet, Øystre Slidre commune, southern Norway.Methods: Two comparable neighbouring areas, each of 4 km 2 , were mapped for species-defined vegetation types. One area was used for model training, the other for model projection. Maximum entropy models were run for six vegetation types, two from each of the ecosystems present in the area: forest, wetland and mountain heath-and shrublands. For each ecosystem, one locally abundant and one locally rare vegetation type were tested. AUC, the area under the receiver operating curve, was used as the model selection criterion. Environmental variables (n = 9) were selected through a backwards selection scheme, and model complexity was kept low. The models were evaluated using independent data.Results: Distribution modelling of vegetation types by local projection gave high AUC values, and the results were supported by the evaluation using independent data. The modelling ability was not affected by ecosystem differences. A negative relationship between the number of points used to train the models and the AUC value before evaluation suggests that models for locally rare vegetation types had better predictive performance than the models for abundant types. This result was not significant after evaluation.Conclusion: Provided that relevant explanatory variables are available at an appropriate scale, and that field-validated training points are available, distribution modelling can be used for local projection of the six tested vegetation types from the boreal-alpine ecotone.

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“…The guidelines for VK25 mapping include detailed instructions for a number of difficult aspects, such as drawing lines between fuzzy borders, deciding on vegetation type among closely related types, and recording the cover of species important for the separation between types (Bryn et al 2015). A mosaic of two different land cover classes can be registered for a polygon when each class covers at least 25% of the area, and if the areas of unique land cover classes are below the minimum size for polygons (for these mapping rules and others, see Rekdal & Larsson 2005).…”

Section: Mapping Of Vegetation Types and Land Cover Classesmentioning

confidence: 99%

Land cover in Norway based on an area frame survey of vegetation types

Bryn

Strand

Angeloff

et al. 2018

Norsk Geografisk Tidsskrift - Norwegian Journal of Geography

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The Norwegian area frame survey of land cover and outfield land resources (AR18X18), completed in 2014, provided unbiased statistics of land cover in Norway. The article reports the new statistics, discusses implications of the data set, and provides potential value in terms of research, management, and monitoring. A gridded sampling design for 1081 primary statistical units of 0.9 km 2 at 18 km intervals was implemented in the survey. The plots were mapped in situ, aided by aerial photos, and all areas were coded following a vegetation type system. The results provide new insights into the cover and distribution of vegetation and land cover types. The statistic for mire and wetlands, which previously covered 5.8%, has since been corrected to 8.9%. The survey results can be used for environmental and agricultural management, and the data can be stratified for regional analyses. The survey data can also serve as training data for remote sensing and distribution modelling. Finally, the survey data can be used to calibrate vegetation perturbations in climate change research that focuses on atmospheric-vegetation feedback. The survey documented novel land cover statistics and revealed that the national cover of wetlands had previously been underestimated.

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Location of plant species in Norway gathered as a part of a survey vegetation mapping programme

Cited by 613 publications

References 3 publications

Distribution modelling of vegetation types based on area frame survey data

Distribution modelling of vegetation types based on area frame survey data

Distribution modelling of vegetation types in the boreal–alpine ecotone

Land cover in Norway based on an area frame survey of vegetation types

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